I personally think that LSA may be a key technology to improving the ability of current search technology to "understand" and answer questions asked in natural language.
Here is information on LSA from Wikipedia:
Latent semantic analysis (LSA) is a technique in natural language processing, in particular in vectorial semantics, invented in 1990  by Scott Deerwester, Susan Dumais, George Furnas, Thomas Landauer, and Richard Harshman. In the context of its application to information retrieval, it is sometimes called latent semantic indexing (LSI).
LSA uses a term-document matrix which describes the occurrences of terms in documents; it is a sparse matrix whose rows correspond to documents and whose columns correspond to terms, typically stemmed words that appear in the documents. A typical example of the weighting of the elements of the matrix is tf-idf: the element of the matrix proportional to the number of times the terms appear in each document, where rare terms are upweighted to reflect their relative importance.
This matrix is common to standard semantic models as well (though it is not necessarily explicitly expressed as a matrix, since the mathematical properties of matrix are not always used).